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Table 12 Performance analysis of CIC-IDS2018 dataset

From: Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

ML

Accuracy

Precision

Recall

F1-score

All feature

Proposal

All feature

Proposal

All feature

Proposal

All feature

Proposal

DT

98.88

99.94

95.25

99.94

95.21

99.94

95.21

99.94

RF

98.74

99.93

98.25

99.93

91.55

99.93

91.55

99.93

ET

98.37

99.94

96.81

99.94

90.38

99.94

90.38

99.94

XGB

99.11

99.87

98.51

99.87

96.51

99.87

96.51

99.87